Free AI-900 Exam braindumps download (AI-900 exam dumps Free)

QUESTION: 21

You are developing a system to predict the prices of insurance for drivers in the United Kingdom.

You need to minimize unintended bias in the system.

What should you do?

Remove information about protected characteristics from the data before sampling.
Take a training sample that is representative of the population in the United Kingdom.
Create a training dataset that uses data from global insurers.
Take a completely random training sample.

Answer(s): B

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QUESTION: 22

HOTSPOT (Drag and Drop is not supported)

To complete the sentence, select the appropriate option in the answer area.

Hot Area:

See Explanation section for answer.

Answer(s): A

Explanation:

Box: object detection

Object detection is similar to tagging, but the API returns the bounding box coordinates (in pixels) for each object found in the image. For example, if an image contains a dog, cat and person, the Detect operation will list those objects with their coordinates in the image. You can use this functionality to process the relationships between the objects in an image. It also lets you determine whether there are multiple instances of the same object in an image.

Reference:

https://docs.microsoft.com/en-us/azure/cognitive-services/computer-vision/concept-object-detection

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QUESTION: 23

Your company is exploring the use of voice recognition technologies in its smart home devices. The company wants to identify any barriers that might unintentionally leave out specific user groups.

This is an example of which Microsoft guiding principle for responsible AI?

accountability
fairness
privacy and security
inclusiveness

Answer(s): D

Explanation:

Inclusive design practices can help system developers understand and address potential barriers in a product environment that could unintentionally exclude people. By addressing these barriers, we create opportunities to innovate and design better experiences that benefit everyone.

Reference:

https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/4-guiding-principles

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QUESTION: 24

HOTSPOT (Drag and Drop is not supported)

Select the answer that correctly completes the sentence.

Hot Area:

See Explanation section for answer.

Answer(s): A

Explanation:

Box: an anomaly detection workload
Anomaly Detector is an AI service with a set of APIs, which enables you to monitor and detect anomalies in your time series data with little machine learning (ML) knowledge, either batch validation or real-time inference.

Anomaly Detector capabilities
With Anomaly Detector, you can either detect anomalies in one variable using Univariate Anomaly Detector, or detect anomalies in multiple variables with Multivariate Anomaly Detector.

Univariate Anomaly Detection
The Univariate Anomaly Detector API enables you to monitor and detect abnormalities in your time series data without having to know machine learning. The algorithms adapt by automatically identifying and applying the best-fitting models to your data, regardless of industry, scenario, or data volume. Using your time series data, the API determines boundaries for anomaly detection, expected values, and which data points are anomalies.